Summary of Cic: a Framework For Culturally-aware Image Captioning, by Youngsik Yun and Jihie Kim
CIC: A Framework for Culturally-Aware Image Captioning
by Youngsik Yun, Jihie Kim
First submitted to arxiv on: 8 Feb 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A novel approach to image captioning is proposed, which focuses on describing cultural elements in images from diverse Asian cultures. The current state-of-the-art models, such as BLIP, excel at generating descriptive sentences but lack the ability to provide detailed captions about traditional clothing and other cultural visual elements. To address this gap, a culturally-aware image captioning (CIC) framework is designed, which combines visual modality with large language models through carefully crafted prompts. The CIC framework consists of three stages: generating questions based on cultural categories from images, extracting cultural visual elements using visual question answering, and generating culturally-aware captions using large language models with the prompts. Evaluation results show that the proposed framework outperforms the image captioning baseline in generating more culturally descriptive captions. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine taking a picture of someone wearing traditional clothing from Japan or India. You want to describe what you’re seeing, but how do you know what cultural significance those clothes hold? Researchers have developed a new way to generate captions that not only describe what’s happening in the image but also highlight the cultural elements depicted. This is important because different cultures have unique traditions and customs that are worth understanding and respecting. |
Keywords
» Artificial intelligence » Image captioning » Question answering
